AI predicts poker bets to three decimals places

Poker is considered one of the most skillful of betting games, but a new study published in the Journal of Gambling Studies reports on an artificial neural network that predicts gambler’s bets to three decimals places.

The system was built by researcher Victor Chan who created a relatively simply backpropogation neural network to predict future plays.

Backprop networks take a bunch of inputs, feed them through layers of loose mathematical simulations of neurons which then make a guess at an output.

Crucially, the network is initially given a set of training data on which it can modify its ‘guesses’ based on how wrong its initial estimation was. The amount of error is fed back through the network and each ‘neuron’ adjusts the strengths of its connections to other neurons to minimise the error next time round.

Chan used the playing patterns of six online Texas Hold ’em players each of whom played more than 100 games each. He entered just an initial series of games for each player to train the network and then asked it to predict how the following plays would go.

…it was to the author‚Äôs surprise that the neural network for M1 upon training turned out to be able to predict a gambler‚Äôs bet amounts in successive games accurately to more than three decimal places of the dollar on average for each of the six gamblers in our data sample across the board.

More importantly, the neural network for M2 upon training was also able to track the temporal trajectory of a gambler‚Äôs cumulative winnings/losses, i.e., successively predict the gambler‚Äôs cumulative winnings/losses, with a similar accuracy again for each of the six gamblers in our data sample across the board.

…the influence of a gambler‚Äôs skills, strategies, and personality on his/her cumulative winnings/losses is almost totally reflected by the pattern(s) of his/her cumulative winnings/losses in the several immediately preceding games.

In other words, from a sample of initial plays, each gambler’s behaviour was almost completely mathematically predictable in the same way across all six people.

Now, if they could only get a neural network to predict plays in strip poker, I think they’d be onto something.

It is interesting to note that the paper mention:
‘It was found that most gamblers played for a short while, whereas only a small proportion of them played as many as around 100 consecutive games, which seemed to be the practical maximum. In fact, from the long videos we managed to identify only six such “persistent” gamblers, totaling 675 games’.
Predicting (“approximating”, really) certain types of human behavior is often really very easy, but notice that this typically requires a substantial number of observations from a fairly narrow, repeated set of circumstances. While the model described in this paper worked very well, it was custom-fit for six individuals.